English(EN) Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation

新的扩散元提示模型增强基础模型泛化能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种扩散元提示（DMP）模型，以提高基础模型的泛化能力。该框架根据自然语言任务描述，从现有提示库中合成新提示，无需原始任务示例或损失。测试时引导策略进一步提高了采样稳定性。DMP在分类、检索和文本到图像生成任务中均表现出改进，在复合分类任务上提高了2.0%，与提示检索方法相比，存储和推理成本降低了90%以上。 AI

影响这项新的提示技术有望显著降低基础模型在各种任务中的成本并提高其泛化能力。

排序理由这是一篇详细介绍基础模型适配新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Deepak Sridhar, Yi Li, Kartikeya Bhardwaj, Shuangjun Liu, Taotao Jing, Yuan Li, Shuai Zhang, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos · 2026-10-09 04:00

用于通用基础模型适应的扩散元提示和引导

arXiv:2610.11067v1 Announce Type: new Abstract: Prompt learning is a popular method for adapting foundation models, but learned prompts are typically task-specific and fail to generalize to new classes, domains, or compositions of tasks. In this paper, we introduce a Diffusion Me…

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用于通用基础模型适应的扩散元提示和引导

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